You will never know whether you have an effective user experience until you have tested it with users. In this course, you’ll learn how to design experiments, how to run experiments, and how to analyze data from these experiments in order to evaluate and validate user experiences. You will work through real-world examples of experiments from the fields of IxD and HCI, understanding issues in experiment design and analysis. You will analyze multiple data sets using recipes given to you in the R statistical programming language -- no prior programming experience is assumed or required. By the end of the course, you will be able to knowledgeably design, run, and analyze your own experiments for putting empirical and statistical weight behind your designs.

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One-Factor Within-Subjects Experiments

In this module, you will learn about one-factor within-subjects experiments, also known as repeated measures designs. The experiment examined will be a within-subjects study of subjects searching for contacts in a smartphone contacts manager, including the analysis of times, errors, and effort Likert-type scale ratings. You will learn counterbalancing strategies to avoid carryover effects, including full counterbalancing, Latin Squares, and balanced Latin Squares. You will understand and analyze data from two-level factors and three-level factors using the paired-samples t-test, Wilcoxon signed-rank test, oneway repeated measures ANOVA, and Friedman test. This module covers lecture videos 19-23.